|
|
|
| Identifying Research Topic Evolutionary Paths Based on Matrix Similarity |
| Huang Han1, Wang Xiaoguang1,2, He Jing1, Wang Hongyu3 |
1.School of Information Management, Wuhan University, Wuhan 430072
2.Big Data Institute, Wuhan University, Wuhan 430072
3.School of Management, Wuhan University of Technology, Wuhan 430070 |
|
|
|
|
Abstract The evolution of research topics is critical for clarifying scientific development and predicting frontiers. The calculation of the similarity of topics in adjacent time periods and identification of their evolutionary paths is the core step in the topic evolution analysis. In this study, the matrix similarity algorithm is innovatively proposed to identify the topic evolution path. Based on the local network structure of the research topic in the co-word network, the similarity of the research topic in terms of words and relations is considered in the calculation of topic similarity. Subsequently, an analytical framework for research topic evolution based on matrix similarity is constructed. Considering piecewise linear representation, the framework divides the data into time periods to build the temporal co-word networks. After identifying the topic communities in the co-word network at each time period using the community discovery algorithm, multi-dimensional feature indexes such as novelty, popularity, core, and maturity are calculated to represent the types of research topics. The evolutionary paths of the research topics are then identified using matrix similarity calculations. Finally, the evolution process of the research topics is visualized by a Sankey diagram and a multi-dimensional strategic coordinate plot. Specifically, this study uses the field of library and information science as an example of empirical analysis. The results show that the proposed method can effectively support the evolution analysis of research topics in a research area and provide methodological support for research decision-making.
|
|
Received: 05 December 2022
|
|
|
|
1 Liu H L, Chen Z W, Tang J, et al. Mapping the technology evolution path: a novel model for dynamic topic detection and tracking[J]. Scientometrics, 2020, 125(3): 2043-2090.
2 Chen B T, Tsutsui S, Ding Y, et al. Understanding the topic evolution in a scientific domain: an exploratory study for the field of information retrieval[J]. Journal of Informetrics, 2017, 11(4): 1175-1189.
3 Huang L, Chen X, Zhang Y, et al. Identification of topic evolution: network analytics with piecewise linear representation and word embedding[J]. Scientometrics, 2022, 127(9): 5353-5383.
4 Zhang X Y, Xie Q, Song C, et al. Mining the evolutionary process of knowledge through multiple relationships between keywords[J]. Scientometrics, 2022, 127(4): 2023-2053.
5 Katsurai M, Ono S. TrendNets: mapping emerging research trends from dynamic co-word networks via sparse representation[J]. Scientometrics, 2019, 121(3): 1583-1598.
6 Jung S, Yoon W C. An alternative topic model based on common Interest authors for topic evolution analysis[J]. Journal of Informetrics, 2020, 14(3): 101040.
7 罗双玲, 张文琪, 夏昊翔. 基于半积累引文网络社区发现的学科领域主题演化分析——以“合作演化”领域为例[J]. 情报学报, 2017, 36(1): 100-110.
8 王晓光, 程齐凯. 基于NEViewer的学科主题演化可视化分析[J]. 情报学报, 2013, 32(9): 900-911.
9 Wang X G, Cheng Q K, Lu W. Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks[J]. Scientometrics, 2014, 101(2): 1253-1271.
10 Chen C M. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the American Society for Information Science and Technology, 2006, 57(3): 359-377.
11 van Eck N J, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping[J]. Scientometrics, 2010, 84(2): 523-538.
12 Aria M, Cuccurullo C. Bibliometrix: an R-tool for comprehensive science mapping analysis[J]. Journal of Informetrics, 2017, 11(4): 959-975.
13 Cobo M J, López-Herrera A G, Herrera-Viedma E, et al. SciMAT: a new science mapping analysis software tool[J]. Journal of the American Society for Information Science and Technology, 2012, 63(8): 1609-1630.
14 王康, 陈悦, 苏成, 等. 多维视角下科学主题演化分析框架[J]. 情报学报, 2021, 40(3): 297-307.
15 陈伟, 林超然, 李金秋, 等. 基于LDA-HMM的专利技术主题演化趋势分析——以船用柴油机技术为例[J]. 情报学报, 2018, 37(7): 732-741.
16 陈翔, 黄璐, 倪兴兴, 等. 基于动态语义网络分析的主题演化路径识别研究[J]. 情报学报, 2021, 40(5): 500-512.
17 Deligiannis P, Vergoulis T, Chatzopoulos S, et al. Visualising scientific topic evolution[C]// Proceedings of the Web Conference 2021. New York: ACM Press, 2021: 468-472.
18 Jiang L, Zhang T, Huang T H. Empirical research of hot topic recognition and its evolution path method for scientific and technological literature[J]. Journal of Advanced Computational Intelligence and Intelligent Informatics, 2022, 26(3): 299-308.
19 Behrouzi S, Sarmoor Z S, Hajsadeghi K, et al. Predicting scientific research trends based on link prediction in keyword networks[J]. Journal of Informetrics, 2020, 14(4): 101079.
20 Choudhury N, Uddin S. Time-aware link prediction to explore network effects on temporal knowledge evolution[J]. Scientometrics, 2016, 108(2): 745-776.
21 Chang P C, Liao T W, Lin J J, et al. A dynamic threshold decision system for stock trading signal detection[J]. Applied Soft Computing, 2011, 11(5): 3998-4010.
22 Kimura A, Kashino K, Kurozumi T, et al. A quick search method for audio signals based on a piecewise linear representation of feature trajectories[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2008, 16(2): 396-407.
23 陈虹枢. 基于主题模型的专利文本挖掘方法及应用研究[D]. 北京: 北京理工大学, 2015: 31-32.
24 Chehab J P, Raydan M. Geometrical properties of the Frobenius condition number for positive definite matrices[J]. Linear Algebra and Its Applications, 2008, 429(8/9): 2089-2097.
25 Mijangos V, Sierra G, Herrera A. A word embeddings model for sentence similarity[J]. Research in Computing Science, 2016, 117(1): 63-74.
26 Mijangos V, Sierra G, Montes A. Sentence level matrix representation for document spectral clustering[J]. Pattern Recognition Letters, 2017, 85: 29-34.
27 Wang E Y, Guo W, Dai L R, et al. Factor analysis based spatial correlation modeling for speaker verification[C]// Proceedings of the 2010 7th International Symposium on Chinese Spoken Language Processing. Piscataway: IEEE, 2011: 166-170.
28 Rafii Z, Pardo B. Music/voice separation using the similarity matrix[C]// Proceedings of the 13th International Society for Music Information Retrieval Conference, 2012: 583-588.
29 Demirci R. Similarity relation matrix-based color edge detection[J]. AEU-International Journal of Electronics and Communications, 2007, 61(7): 469-477.
30 王康, 高继平, 潘云涛, 等. 多位态研究主题识别及其演化路径方法研究[J]. 图书情报工作, 2021, 65(11): 113-122.
31 祝娜, 王芳. 基于主题关联的知识演化路径识别研究——以3D打印领域为例[J]. 图书情报工作, 2016, 60(5): 101-109.
32 Law J, Bauin S, Courtial J P, et al. Policy and the mapping of scientific change: a co-word analysis of research into environmental acidification[J]. Scientometrics, 1988, 14(3-4): 251-264.
33 Han X Y. Evolution of research topics in LIS between 1996 and 2019: an analysis based on latent Dirichlet allocation topic model[J]. Scientometrics, 2020, 125(3): 2561-2595. |
|
|
|
